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16th European conference on computer vision, ECCV 2020

作者： Wen, Xin Li, Biying Guo, Haiyun Liu, Zhiwei Hu, Guosheng Tang, Ming Wang, Jinqiao National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China ObjectEye Inc. Beijing China NEXWISE Co. Ltd. Guangzhou China Anyvision Belfast United Kingdom

ISBN: (纸本)9783030585914

Estimating age from a single facial image is a classic and challenging topic in computer vision. One of its most intractable issues is label ambiguity, i.e., face images from adjacent age of the same person are often indistinguishable. Some existing methods adopt distribution learning to tackle this issue by exploiting the semantic correlation between age labels. Actually, most of them set a fixed value to the variance of Gaussian label distribution for all the images. However, the variance is closely related to the correlation between adjacent ages and should vary across ages and identities. To model a sample-specific variance, in this paper, we propose an adaptive variance based distribution learning (AVDL) method for facial age estimation. AVDL introduces the data-driven optimization framework, meta-learning, to achieve this. Specifically, AVDL performs a meta gradient descent step on the variable (i.e. variance) to minimize the loss on a clean unbiased validation set. By adaptively learning proper variance for each sample, our method can approximate the true age probability distribution more effectively. Extensive experiments on FG-NET and MORPH II datasets show the superiority of our proposed approach to the existing state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： Probability distributions

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Learning to Predict Context-Adaptive Convolution for Semantic Segmentation 16th

Learning to Predict Context-Adaptive Convolution for Semanti...

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16th European conference on computer vision, ECCV 2020

作者： Liu, Jianbo He, Junjun Qiao, Yu Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory The Chinese University of Hong Kong Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SenseTime Research Hong Kong

ISBN: (纸本)9783030585945

Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods demonstrate that using global context for re-weighting feature channels can effectively improve the accuracy of semantic segmentation. However, the globally-sharing feature re-weighting vector might not be optimal for regions of different classes in the input image. In this paper, we propose a Context-adaptive Convolution Network (CaC-Net) to predict a spatially-varying feature weighting vector for each spatial location of the semantic feature maps. In CaC-Net, a set of context-adaptive convolution kernels are predicted from the global contextual information in a parameter-efficient manner. When used for convolution with the semantic feature maps, the predicted convolutional kernels can generate the spatially-varying feature weighting factors capturing both global and local contextual information. Comprehensive experimental results show that our CaC-Net achieves superior segmentation performance on three public datasets, PASCAL Context, PASCAL VOC 2012 and ADE20K. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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Conditional Sequential Modulation for Efficient Global Image Retouching 16th

Conditional Sequential Modulation for Efficient Global Image...

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16th European conference on computer vision, ECCV 2020

作者： He, Jingwen Liu, Yihao Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT - SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030586003

Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be accomplished by a series of image processing operations. In this paper, we investigate some commonly-used retouching operations and mathematically find that these pixel-independent operations can be approximated or formulated by multi-layer perceptrons (MLPs). Based on this analysis, we propose an extremely light-weight framework - Conditional Sequential Retouching Network (CSRNet) - for efficient global image retouching. CSRNet consists of a base network and a condition network. the base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector. To realize retouching operations, we modulate the intermediate features using Global Feature Modulation (GFM), of which the parameters are transformed by condition vector. Benefiting from the utilization of 1 × 1 convolution, CSRNet only contains less than 37 k trainable parameters, which is orders of magnitude smaller than existing learning-based methods. Extensive experiments show that our method achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively. Code is available at https://***/hejingwenhejingwen/CSRNet. © 2020, Springer Nature Switzerland AG.

关键词： Pixels

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Semantic-Aware chinese Zero Pronoun Resolution with Pre-trained Semantic Dependency Parser 19

Semantic-Aware Chinese Zero Pronoun Resolution with Pre-trai...

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19th China National conference on Computational Linguistics, CCL 2020

作者： Zhang, Lanqiu Shen, Zizhuo Shao, Yanqiu Beijing Language and Culture University Beijing China

ISBN: (纸本)9783030630300

Deep learning-based chinese zero pronoun resolution model has achieved better performance than traditional machine learning-based model. However, the existing work related to chinese zero pronoun resolution has not yet well integrated linguistic information into the deep learning-based chinese zero pronoun resolution model. this paper adopts the idea based on the pre-trained model, and integrates the semantic representations in the pre-trained chinese semantic dependency graph parser into the chinese zero pronoun resolution model. the experimental results on OntoNotes-5.0 dataset show that our proposed chinese zero pronoun resolution model with pre-trained chinese semantic dependency parser improves the F-score by 0.4% compared with our baseline model, and obtains better results than other deep learning-based chinese zero pronoun resolution models. In addition, we integrate the BERT representations into our model so that the performance of our model was improved by 0.7% compared with our baseline model. © 2020, Springer Nature Switzerland AG.

关键词： Deep learning

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OSTER: An Orientation Sensitive Scene Text Recognizer with CenterLine Rectification 5th

OSTER: An Orientation Sensitive Scene Text Recognizer with C...

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5th Asian conference on pattern recognition, ACPR 2019

作者： Feng, Zipeng Du, Chen Wang, Yanna Xiao, Baihua The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030414030

Scene texts in China are always arbitrarily arranged in two forms: horizontally and vertically. these two forms of texts exhibit distinctive features, making it difficult to recognize them simultaneously. Besides, recognizing irregular scene texts is still a challenging task due to their various shapes and distorted patterns. In this paper, we propose an orientation sensitive network aiming at distinguishing between chinese horizontal and vertical texts. the learned orientation is then passed into an attention selective network to adjust the attention maps of the sequence recognition model, leading it working for each type of texts respectively. In addition, a lightweight centerline rectification network is adopted, which enables the irregular texts more readable while no redundant labels are needed. A synthetic dataset named SCTD is released to support our training and evaluate the proposed model. Extensive experiments show that the proposed method is capable of recognizing arbitrarily-aligned scene texts accurately and efficiently, achieving state-of-the-art performance over a number of public datasets. © Springer Nature Switzerland AG 2020.

关键词： Character recognition

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第五届中国模式识别与计算机视觉大会

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智能系统学报 2022年第3期17卷 575-575页

第五届中国模式识别与计算机视觉大会(the 5th chinese conference on pattern recognition and computer vision,PRCV 2022)将于2022年10月14-17日在深圳举办。PRCV 2022由中国人工智能学会(CAAI)、中国计算机学会(CCF)、中国自动化学会(CAA)和中国图象图形学学会(CSIG)联合主办;由南方科技大学和深圳职业技术学院共同承办;并由香港浸会大学、香港中文大学(深圳)、哈尔滨工业大学(深圳)、中国科学院深圳先进技术研究院和中山大学联合承办,是国内顶级的模式识别和计算机视觉领域学术盛会。

关键词：计算机视觉中国自动化学会中国计算机学会南方科技大学模式识别中国科学院人工智能学会 PRCV

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第五届中国模式识别与计算机视觉大会

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智能系统学报 2022年第1期17卷 87-87页

第五届中国模式识别与计算机视觉大会(the 5th chinese conference on pattern recognition and computer vision,PRCV 2022)将于2022年10月14日至10月17日在深圳举办。PRCV 2022由中国人工智能学会(CAAI)、中国计算机学会(CCF)、中国自动化学会(CAA)和中国图象图形学学会(CSIG)联合主办;由南方科技大学和深圳职业技术学院共同承办;并由香港浸会大学、香港中文大学(深圳)、哈尔滨工业大学(深圳)、中国科学院深圳先进技术研究院和中山大学联合承办,是国内顶级的模式识别和计算机视觉领域学术盛会。

关键词：计算机视觉中国自动化学会中国计算机学会南方科技大学模式识别中国科学院人工智能学会 PRCV

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Learning Feature Embeddings for Discriminant Model Based Tracking 16th

Learning Feature Embeddings for Discriminant Model Based Tra...

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16th European conference on computer vision, ECCV 2020

作者： Zheng, Linyu Tang, Ming Chen, Yingying Wang, Jinqiao Lu, Hanqing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Shenzhen Infinova Limited Shenzhen China ObjectEye Inc. Beijing China

ISBN: (纸本)9783030585549

After observing that the features used in most online discriminatively trained trackers are not optimal, in this paper, we propose a novel and effective architecture to learn optimal feature embeddings for online discriminative tracking. Our method, called DCFST, integrates the solver of a discriminant model that is differentiable and has a closed-form solution into convolutional neural networks. then, the resulting network can be trained in an end-to-end way, obtaining optimal feature embeddings for the discriminant model-based tracker. As an instance, we apply the popular ridge regression model in this work to demonstrate the power of DCFST. Extensive experiments on six public benchmarks, OTB2015, NFS, GOT10k, TrackingNet, VOT2018, and VOT2019, show that our approach is efficient and generalizes well to class-agnostic target objects in online tracking, thus achieves state-of-the-art accuracy, while running beyond the real-time speed. Code will be made available. © 2020, Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Gaze Estimation with Multi-Scale Channel and Spatial Attention 2020

Gaze Estimation with Multi-Scale Channel and Spatial Attenti...

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Proceedings of the 2020 9th International conference on Computing and pattern recognition

作者： Song Liu Danping Liu Haiyang Wu Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences School of Microelectronics and Communication Engineering Chongqing University

ISBN: (纸本)9781450387835

Gaze estimation is well established as a significant research topic in computer vision given its importance for different applications. Recent studies demonstrate that other regions of the face beyond the two eyes contain valuable information for gaze estimation. Motivated by these works, we propose a novel and powerful deep convolutional network with multi-scale channel and spatial attention, which only takes the full-face image as input without additional modules to detect the eyes and estimate the head pose, to handle the gaze estimation task. It uses multi-scale channel and spatial information to adaptively select and increase important features and suppress some unnecessary facial regions which may not contribute to estimate gaze. By rigorously evaluating our module, we show that our method significantly outperforms the state-of-the-art for 3D gaze estimation on multiple public datasets.

关键词： Eye tracking attention mechanism appearance-based gaze estimation machine learning

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Visual Counting of Traffic Flow from a Car via Vehicle Detection and Motion Analysis 5th

Visual Counting of Traffic Flow from a Car via Vehicle Detec...

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5th Asian conference on pattern recognition, ACPR 2019

作者： Kolcheck, Kevin Wang, Zheyuan Xu, Haiyan Zheng, Jiang Yu Indiana University Purdue University Indianapolis IndianapolisIN46202 United States Fukuoka Institute of Technology Fukuoka Japan

ISBN: (纸本)9783030414030

Visual traffic counting so far has been carried out by static cameras at streets or aerial pictures from sky. this work initiates a new approach to count traffic flow by using populated vehicle driving recorders. Mainly vehicles are counted by a camera moves along a route on opposite lane. Vehicle detection is first implemented in video frames by using deep learning YOLO3, and then vehicle trajectories are counted in the spatial-temporal space called motion profile. Motion continuity, direction, and detection missing are considered to avoid multiple counting of oncoming vehicles. this method has been tested on naturalistic driving videos lasting for hours. the counted vehicle numbers can be interpolated as a flow of opposite lanes from a patrol vehicle for traffic control. the mobile counting of traffic is more flexible than the traffic monitoring by cameras at street corners. © Springer Nature Switzerland AG 2020.

关键词： computer vision

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